Taiwan Stock Exchange Research Articles

The Impact of Institutional Shareholdings on Price Limits

We explore whether institutional shareholders would affect the price limits in Taiwan due to that the price limit system in Taiwan is lower than those of other countries. In this study, We reveal that various institutional shareholding ratios including domestic institutional shareholding ratio and foreign institutional shareholding ratio significantly affect several stock limit ratios including the stock limit ratio, stock price up-limit ratio, and stock price down-limit ratio rarely concerned comprehensively in the relevant studies. In addition, we argue that our interesting findings are likely interpreted as follows: First, domestic institutions might buy stocks to price up-limit to improve their performance. Second, institutional investors might beat their competitors by selling competitors’ stocks to price down-limits. Third, foreign institutions might sell stocks to price down-limits after taking many short positions in index futures. We deduce that the Taiwan price limit system might provide opportunities for institutional investors, stakeholders, and even insiders to manipulate share prices because the price limit likely occurs in Taiwan Stock Exchange.

A Multi-attribute Fuzzy Fluctuation Time Series Model Based on Neutrosophic Soft Sets and Information Entropy

Most existing forecasting models abstract logical rules that are based on historical discrete states in time series, and inconsistencies between these discrete states are rarely described quantitatively. In this paper, a multi-attribute fuzzy fluctuation time series-forecasting model based on neutrosophic soft sets (NSSs) and information entropy is proposed, which describes the complex changes of the logical rule training stage from the two characteristics of the state and volatility. The innovation and advantages of the model are mainly as follows: (1) The NSSs which have multi-attribute mapping and multi-dimensional expression functions can depict the complex state of multiple attributes in a specific period of time and thus characterise the state of the stock market clearly. (2) Using information entropy to quantify the degree of inconsistency of stock market fluctuations at a certain time which reflect the characteristics of volatility in the stock market effectively. (3) The similarity measure is used to find the optimal rule from the dimensions of state and volatility. To verify the validity of this model, this paper takes the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Experiments show that the model has a stable prediction performance for different data sets. Meanwhile, the prediction error is compared with other methods, which proves that the model has better prediction accuracy and versatility.

Type-1 possibilistic fuzzy forecasting functions

Type-1 Fuzzy Functions (T1FFs) were developed by Turksen as an alternative fuzzy inference system (FIS) and have been commonly used in forecasting problems. The main advantages of T1FFs are that they are free of rules and easy to implement. Thus, they have recently been an attractive tool for researchers. T1FFs start with clustering the inputs using the fuzzy c-means (FCM) clustering algorithm. Later, the degrees of membership and its nonlinear transformations are included into the input matrix for each cluster. Thus, as many input matrices are obtained as the number of clusters. Finally, the outputs are combined using the degrees of membership of the new observations. Because gathering objects in the same cluster as homogeneously as possible is an important task for a clustering algorithm, the possibilistic FCM is adapted to T1FFs in order to overcome the FCM’s limitations in the proposed method. We used 14 financial datasets and a beer consumption dataset to verify the forecasting performance of the proposed method. For example, the proposed method outperformed the other selected forecasting methods for the Taiwan Stock Exchange time-series datasets in terms of the mean of root mean square errors.

A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.

A novel fuzzy-Markov forecasting model for stock fluctuation time series

In order to reveal intrinsic fluctuation rules hidden in a stock market time series dataset with noise, a novel forecasting model combining Markov chain theory with fuzzy set theory is proposed in this study. A fuzzified one-step transition matrix of Markov Chain in the paper represents inherent rules of historical fluctuation. Comparing with existing models, the advantage of the proposed model is that transition matrix can express the relationship between history and current flexibly while the introduction of fuzzy theory can help to alleviate noises. Therefore, the proposed model could handle complex patterns during state transitions and the relatively simple forecasting algorithm could reduce the calculation cost. We apply the proposed method to forecast well-known stock indexes such as (Taiwan Stock Exchange Capitalization Weighted Stock Index) TAIEX, (Shanghai Stock Exchange Composite Index) SHSECI and so on. Experimental results demonstrate that our proposed method outperforms other traditional models.

Exploring the learning effectiveness of financial literacy from the microworld perspective: Evidence from the simulated transactional interactive concurrent system

AbstractBecause the microworld can create a learning environment that allows learners to interact with the real world, the view of the microworld has been widely used in education and training. In this study, the simulated transactional interactive concurrent system (STICS) was used to provide an environment that simulates the stock market trading of the Taiwan Stock Exchange; the goal is to explore the effectiveness of using STICS in enhancing the financial literacy of students. This study adopts a quasi‐experimental design approach and divides students into two groups. The intervention group was made up of 42 students who learned financial knowledge aided by STICS, with a comparison group consisting of 43 students who learned with traditional lecturing and dictating methods. The study found that learners who used the STICS as an aid to learn financial knowledge had significantly better learning effectiveness than those who did not use STICS, in terms of motivation, attitude, and achievement. The intervention group also made significant improvements in average scores in learning motivation and achievement after 8 weeks of experimental teaching and learning.

Whose trades move stock prices? Evidence from the Taiwan Stock Exchange

Analyzing firms listed on the Taiwan Stock Exchange, this research examines whose trades move stock prices. To emphasize the roles of different types of investors, we construct the weighted price contribution (WPC), explore sensitivities of the price contribution to order submission behavior, and then test whether their order aggressiveness and trade size, along with current price contribution, affect future stock returns. Our empirical results reveal several new findings. First, individual traders account for more than 80% of WPC, while institutional investors make up less than 20% of WPC. Second, although mutual funds make up the smallest proportion of WPC, their WPC per order (or per lot) and their order aggressiveness are both the highest among all types of traders. Finally, price contributions generated by large orders of professional institutions are closely related to future stock performance, although they account only for a small proportion of total WPC.

Construction of a Supplier Evaluation Model in the Aerospace Sector

Abstract The aerospace sector has a demand for high-precision and expensive machine tools that are characterized by a high entry threshold, high risks, and a long payback period. To ensure product quality and the reduction of operating costs, it is imperative that manufacturers in this sector develop an appropriate supplier evaluation and management mechanism for machine tools. Therefore, this study presents a new two-stage supplier evaluation model for the aerospace sector. In the first stage, a hierarchical structure that comprises three evaluation criteria and eleven subcriteria is constructed. In the second stage, suppliers are appraised and selected through the analytic hierarchy process. As exemplified by the purchase of high-precision and expensive machine tools by Taiwan’s Aero Win Technology Corporation (listed in the Taiwan Stock Exchange), this study conducts a feasibility and sensitivity analysis with respect to the supplier evaluation model. The three criteria are ranked in the order of decreasing importance as follows: quality > cost > delivery. The results of this research have useful implications for the evaluation policy of machine tool suppliers in the aerospace sector.

A Study of Association between Taiwan’s Corporate Governance Appraisal and Financial Performance: Evidence from Taiwan Listed Companies

To enable all listed companies to gradually upgrade and implement corporate governance, the appraisals are promoted to assist investors through the comparison of corporate governance appraisal (CGA) in Taiwan’s market. Using a panel data is based on the companies listed on the Taiwan Stock Exchange during the period 2014-2016; this paper provides evidence that earnings management is affected negatively by corporate governance quality.
 
 This is expected to guide healthy competition between enterprises and strengthen corporate governance. Recent studies have pointed out that managers are more favorable to their actions due to weak corporate governance. While most studies explored the relationship between corporate governance and financial performance, few studies have included in corporate governance appraisal (CGA).
 
 This study examines how CGA in Taiwan listed companies will affect their earnings quality and this study uses earnings management (EM) as measure of financial performance. In addition, reference is made to the Big 4 accounting firms to explain the consequences of CGA and, specifically, its effect on the quality of financial statements. The empirical results show that CGA and earnings management have a significantly negative correlation. In addition, the CGA of companies audited by the Big 4 indicate that those with better earnings quality also conduct less earnings management.

The Determinants of the Stock Price Performance of Analyst Recommendations

This study examines the cross-sectional determinants of the price reaction to analysts’ recommendations disseminated through various type of media and for firms listed in Taiwan stock markets. We measure abnormal returns using the market model of event study. Based on the type of media (traditional media/social media) and the type of exchange (Taiwan Stock Exchange (TWSE)/Taipei Exchange (TPEx)), we classify the combined sample observations into four samples and run quantile regressions to investigate whether the relation will be uniform across various quantile levels. Our results show that the relation between firm characteristics and cumulative abnormal returns is not homogeneous across various quantiles of abnormal returns. Our evidence indicates that in general the relation tends to be stronger for firms at higher performance quantile levels and tends to be more pronounced for TWSE firms. The strongest relation is found for the Traditional/TWSE sample, where the abnormal returns are positively related to insider ownership and prior-period earnings, and negatively related to institutional shareholding and price-to-book ratio for firms in the highest abnormal performance quantile.

Widening price limit effects: evidence from an emerging stock market

ABSTRACTThis study investigates the impact of the widening of price limits on the Taiwan Stock Exchange, from 7% to 10%, by comparing the differences in performance with the different price limit levels. The empirical results show that after the event: (1) the quoted and effective spreads increased, including the components of realized spread and information asymmetry; (2) the trade-related and quote-related standard deviations and the price discovery became larger. Our outcomes indicate that the widening of price limits was disadvantageous to liquidity, but helpful for price discovery. These results imply that wider price limits are not necessarily always better than narrow price limits in every respect, but that the optimal level of price limits should be based upon the regulators’ intended goals.

Price limit changes, order decisions, and stock price movements: an empirical analysis of the Taiwan Stock Exchange

This study examines the relationships among price limit changes, order submission decisions, and stock returns in the Taiwan Stock Exchange. Specifically, we first investigate whether price limit changes affect overall investors’ order aggressiveness and trade size. We then analyze the relationships across trader types. Finally, we analyze the effects on future stock returns. Our analysis yields the following findings. (1) Stock traders’ order submission decisions are quite different in the post- versus pre-event periods, as they place orders at more aggressive prices but with smaller sizes after the price limit changes. (2) Buy and sell orders demonstrate asymmetric effects with sell orders responding to a policy change in a more diversified way than do buy orders. (3) The sensitivities of order submission decisions to a policy change are significantly different across trader classes. Foreign investors are conservative while investment trusts drastically change their post-event order submission behaviors. (4) Professional institutions’ trade size is a better predictor of future stock returns than order aggressiveness.

Type-1 recurrent intuitionistic fuzzy functions for forecasting

In this study, a novel forecasting method that employs intuitionistic fuzzy c-means clustering and a grey wolf optimizer in recurrent type-1 fuzzy functions (R-T1FFs) is introduced. R-T1FFs, which adapt to the moving average (MA) along with an autoregressive model (AR), were introduced recently to improve the forecasting performances of type-1 fuzzy functions, which adapted solely to the AR model. Because the objective function of R-T1FFs was non-derivative, particle swarm optimization was used to estimate the coefficients of the model. Because R-T1FFs calculate the disturbance terms for the MA model recursively, the model is computationally intensive. Therefore, the proposed method employs a grey wolf optimizer for training processes. Examples in the literature demonstrate that the grey wolf optimizer performs well when obtaining model coefficients because convergence is faster owing to a continuous reduction in the search space and reduced storage requirements. Another contribution of the proposed method is an improved forecasting accuracy, which is obtained by quantifying the hesitancy with which an observation belongs to a cluster given a certain degree of membership. A comparison of the proposed method with numerous existing methods is conducted for 12 practical time series datasets as applications. The accuracy of the proposed method is investigated using root-mean-squared error, mean absolute percentage error, and correlation coefficients; further, whether it produces significantly better outcomes than the existing methods is verified using paired t-tests. For example, it is clear by looking at the average RMSEs of the methods that the best forecasting performance is produced by the proposed method for Taiwan stock exchange datasets.

Does “Resetting” by Changing Corporate Name or Industry Category Appeal to Institutional Investments?

We investigate whether firms changing their names or industry categories once and more than once would affect institutional shareholdings. By utilizing 5,733 observations of the Taiwan Stock Exchange listed firms, we apply multiple regression models firstly and Petersen regression models for further investigation to enhance the robustness of the empirical results. We then disclose several important findings as follows. First, institutional investors might not prefer holding the shares of the firms changing their names more than once. We infer that the performances of the firms changing names more than once might be doubtful. Second, institutional investors might decrease the shareholdings of the firms with industry categories changed. We claim that institutional investors might suspect these firms probably existing corporate governance issues. Besides, we argue that, to our best understanding, this study might fill the gap in the existing literature due to that the issues, firms changing their names or industry categories once or more than once, seem rarely explored in the relevant studies.

A new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithm

This article acquaints a new method to forecast the time series dataset based on neutrosophic-quantum optimization approach. This study uses neutrosophic set (NS) theory to represent the inherited uncertainty of time series dataset with three different memberships as truth, indeterminacy and false. We refer such representations of time series dataset as neutrosophic time series (NTS). This NTS is further utilized for modeling and forecasting time series dataset. Study showed that the performance of NTS modeling approach is highly dependent on the optimal selection of the universe of discourse and its corresponding intervals. To resolve this issue, this study selects quantum optimization algorithm (QOA) and ensembles with the NTS modeling approach. QOA improves the performance of the NTS modeling approach by selecting the globally optimal universe of discourse and its corresponding intervals from the list of local optimal solutions. The proposed hybrid model (i.e., NTS-QOA model) is verified and validated with datasets of university enrollment of Alabama (USA), Taiwan futures exchange (TAIFEX) index and Taiwan Stock Exchange Corporation (TSEC) weighted index. Various experimental results signify the efficiency of the proposed NTS-QOA model over existing benchmark models in terms of average forecasting error rates (AFERs) of 0.44%, 0.066% and 1.27% for the university enrollment, TAIFEX index and TSEC weighted index, respectively.

A High-Order Neutrosophic-Neuro-Gradient Descent Algorithm-Based Expert System for Time Series Forecasting

This article proposed a novel hybrid time series forecasting model using neutrosophic set (NS) theory, artificial neural network (ANN) and gradient descent algorithm. This study deals with three main problems of time series dataset, viz., representation of time series dataset using NS, three degrees of memberships of NS together, and generation of the forecasting results. To resolve these three domain specific problems, this study advocated the application of neutrosophic-neuro-gradient approach. NS theory was utilized to represent the uncertainty associated with time series dataset and was referred to as neutrosophic time series (NTS). In NTS, various decision rules were created in the form of IF-THEN rules, which were termed as neutrosophic entropy decision rules (NEDRs). An ANN-based architecture took NEDRs as input to evolve the forecasting results. To improve the performance of ANN and to obtain optimal forecasting results, this study additionally utilized the gradient descent algorithm to minimize the differences between the calculated and target output values during the simulation. The proposed model was verified and validated with three different datasets, including TAIFEX index, university enrollment dataset of Alabama and Taiwan Stock Exchange Corporation (TSEC) weighted index. Experimental results showed that the proposed model outperformed existing benchmark models with average forecasting error rates of 1.02%, 0.74% and 1.27% for the TAIFEX, university enrollment and TSEC, respectively.

The Effects of International Financial Reporting Standards on Financial Reporting Quality

The purpose of this study is to investigate whether the financial reporting under International Financial Reporting Standards (IFRS) has more quality than local GAAP for firms listed on Taiwan stock exchange. Financial Reporting Quality is measured in this study by three attributes of earnings introduced in previous literature, namely: 1) earnings management expressed as managing towards positive earnings and earnings smoothing and, 2) timely loss recognition expressed as the asymmetric incorporation of economic gains and losses and large negative net income, and 3) value relevance. Ordinary Least Square (OLS) Regression analysis, Ztest, and Binary Logistic Regression are employed to investigate the pre-IFRS (2008- 2010) and post-IFRS (2012-2014) adoption periods on value relevance, earnings management, and timely loss recognition. The study employs a sample of 426 manufacturing firms from 8 industries listed on Taiwan stock exchange. The study finds that firms adopting IFRS evidence less earnings smoothing. However, there is no significant difference in either the timely loss recognition or the value relevance of accounting information between the pre and post-adoption periods. This study contributes to the literature by using data from an emerging market. It provides an insight to practitioners, international standard setters and regulators into the international debate on the effects of the switch from local GAAP to IFRS on Financial Reporting Quality.

The impact of post-trade transparency on price efficiency and price discovery

PurposeThe purpose of this paper is to examine the impact of post-trade transparency on price efficiency and price discovery.Design/methodology/approachThe authors use an exogeneous change in market transparency in the Taiwan Stock Exchange that mandates the disclosure of unexecuted orders of the five best bid and ask prices after each trade, and conduct an event study analysis.FindingsAfter the change, price efficiency enhances for both large and small firms, although the impact on stock prices is greater when the firm is larger. The authors also find that post-change trading reveals more private information for large firms but more public information for small firms. The findings support the view that transparency has a positive impact on market quality.Originality/valueThe paper adds to a large body of literature investigating the relationship between transparency and market behavior, especially the ongoing debate about whether trading transparency positively affects price dynamics. The findings also have important policy implications for the regulators.

Corporate governance and target price accuracy

The purpose of this study is to examine whether the quality of corporate governance has an impact on target price accuracy. We explore the relationship between target price accuracy and ownership structure, board composition, and disclosure using a sample of 17,334 target price observations in the Taiwan Stock Exchange between 2007 and 2016. We find that strong corporate governance improves target price accuracy. We further show that firms with better corporate governance experience a stronger market reaction to their target price announcements.

A New Approach to Multiple Time Series Prediction Using MIMO Fuzzy Aggregation Models with Modular Neural Networks

We present a new approach to multiple time series (MTS) prediction using many-inputs many-outputs (MIMO) fuzzy aggregation models (FAM) with modular neural networks (MNNs). We propose different FAM to generate the forecast outcome of MTS. Several representative approaches to MIMO-FAM are considered. The first FAM is designed with the use of adaptive neuro-fuzzy inference systems (ANFIS) using subtractive clustering (referred to as ANFIS-SC) and fuzzy C-means (referred to as ANFIS-FCM). The second FAM is developed based on Type-1 fuzzy inference systems, while the third FAM exploits interval Type-2 fuzzy inference systems. We design different MNN architectures, and the learning is carried out using backpropagation algorithms. The MTS used in the experiments concerned publicly available data including the Mexican Stock Exchange, National Association of Securities Dealers Automated Quotation and Taiwan Stock Exchange time series. The FAM are compared on the basis of the prediction errors based on commonly used performance indexes, such as the mean absolute error, the mean square error and the root-mean-square error. Simulation results demonstrate the effectiveness of the proposed methods.